Method, system and computer readable storage media for determining articulation parameters

ABSTRACT

A method, system and computer readable storage media for determining articulator parameters. A dentist may use a mobile device such as a smart phone to quickly and inexpensively visualize a 3D model of a patient&#39;s face and jaw, with a depth sensor being used to detect depth information. Articulator parameters may then be obtained to be used for treatment planning based on an analysis of the correct functioning of the patient&#39;s teeth.

FIELD OF THE INVENTION

The present application relates generally to a method, a system andcomputer readable storage media for determining articulation parametersand, more particularly, to a method, system and computer readablestorage media for utilizing 3D face detection methods to determinearticulation parameters of a patient.

BACKGROUND OF THE INVENTION

Dentists may use articulators having upper and lower components to whichmaxillary and mandibular casts may be attached to reproduce the staticrelationship of a patient's maxilla to the patient's mandible. This maybe used to study individual teeth and full dental arches for diagnosisand/or treatment planning as well as to allow the adjustment of fixedand removable prostheses and dental restorations.

A facebow may be used to set up an articulator wherein a determinationof the movements of the patient's temporomandibular joint may be carriedout. Herein, the facebow may be positioned in the ears to transfer anarbitrary hinge axis location by referencing an approximation using thepatient's ears. The facebow may also be aligned to the face of thepatient with a mouth piece. A fixed frame carrying adjustable pivotjoints may be used for supporting the facebow; along with positivelydriven positioning mechanisms for adjusting the pivot joints to providea desired position and aspect of the facebow.

The facebow may transfer the relationship between the maxillary arch andtemporomandibular joint to a cast. It may record the maxilla'srelationship to the External Acoustic Meatus, in a hinge axis and aid inmounting a maxillary cast on the articulator.

The patient may also perform various chewing movements (lateral,forward/backward movements of the jaw etc.), allowing the dentist toread certain values on the facebow for use in for example a commercialarticulator.

This requires a lot of time and presents an unpleasant procedure for thepatient. It would therefore be useful to quickly and automaticallydetermine articulation parameters without the use of a physical facebowor physical articulator.

U.S. Pat. No. 9,336,336B2 discloses using face detection for “smile”design applications. Herein, a dental restoration may be designed for apatient, by providing one or more 2D images, with at least one 2D imageincluding at least one facial feature; providing a 3D virtual model of apart of the patient's oral cavity and arranging one of the 2D imagesrelative to the 3D virtual model. The 3D virtual model and the 2D imageare both visualized in the 3D space; and a restoration is modelled onthe 3D virtual model such that it fits the facial feature of the atleast one 2D image.

U.S. Pat. No. 9,642,686B1 discloses a method and a system for recordingcharacteristics of an occlusal arch of a patient using a portablecomputing device. Herein, a virtual facebow may be used to recordcharacteristics of an occlusal arch of a patient using a facialalignment image having a set of crosshairs. The recorded characteristicsmay then be used to replicate the alignment of the patient's occlusalarch with a maxillary cast in a lab stand, and the maxillary cast maythen be moved to an articulator for the production of customized dentalprosthetics for the patient.

PCT Application No. WO201367606 discloses using digital elements toarticulate a model into a physical articulator wherein a physical studymodel is arranged in an articulator and a direction of a canine line andthe occlusion plane are determined on the study model.

U.S. Pat. No. 8,706,672B2 discloses a computer assisted method ofcreating a custom tooth using facial analysis comprising obtaining dataabout an area which is to be treated and data about a face of a patient,performing an analysis of the data to determine properties of at leastthe face of the patient and creating a modified tooth set-up using a setof stored rules which make use of the determined facial properties.

These methods either do not utilize depth information or employ the useof x-ray images which may not be ideal.

SUMMARY OF THE INVENTION

Existing limitations associated with the foregoing, as well as otherlimitations, can be overcome by a method, system and computer readablestorage media for determining articulation parameters.

In an aspect herein, the present invention may provide a method fordetermining articulation parameters, the method comprising: receivingimages of a patient's face; determining fixed and/or moving points inthe patient's face based on the received images, calculating thearticulator parameters based on the determined fixed and/or movingpoints.

In another aspect herein, the method may further comprise one or more ofthe following steps: (i) wherein the articulator parameters are chosenfrom the group consisting of (1) Sides of a Bonwill triangle, (2)Intercondylar distance, (3) Balkwill angle, (4) Sagittal condylar pathinclination, (5) Bennett angle, (6) Initial Bennett movement and (7)Curve of Spee, (ii) further comprising reconstructing a 3D model of aface of the patient from the received images, (iii) further comprisingsuperimposing a scan of the intraoral cavity of the patient on the 3Dmodel, (iv) wherein the determining step is achieved by feature analysis(v), wherein the feature analysis includes the use of deep learning todetermine fixed and/or moving points, (vi) wherein the fixed pointsinclude a location of a temporomandibular joint. (vii) wherein thecalculating step is achieved using geometrical distances and/or anglesmeasured using the fixed and/or moving points, (viii) further comprisingusing the calculated articulator parameters to fill a virtualarticulator with articulation values, (ix) further comprising analyzingthe functioning of teeth based on the articulator parameters, (x)further comprising producing a restoration or a treatment plan based onthe articulator parameters.

In yet another aspect, the present invention may provide a computerimplemented method for determining articulation parameters, thecomputer-implemented method comprising: training, using one or morecomputing devices and a plurality of training images, a deep neuralnetwork to map one or more fixed and/or moving points in at least oneportion of each training image to one or more highest locationprobability values of a location probability vector; receiving, by theone or more computing devices, images of a patient's face including atleast one jaw; identifying, using the trained neural network, fixedand/or moving points based on one or more output location probabilityvalues of the deep neural network, and calculating the articulatorparameters based on the determined fixed and/or moving points.

In another aspect herein, the computer implemented method may furtherprovide a method wherein the deep neural network is a convolutionalneural network.

In yet another aspect herein, a system may be provided for determiningarticulation parameters, the system comprising at least one processorconfigured to perform the steps of: receiving images of a patient'sface; determining fixed and/or moving points in the patient's face basedon the received images, and calculating the articulator parameters basedon the determined fixed and/or moving points.

The system may also provide one or more combinations of the following:(i) wherein the processor is further configured to choose thearticulator parameters from the group consisting of (1) Sides of aBonwill triangle, (2) Intercondylar distance, (3) Balkwill angle, (4)Sagittal condylar path inclination, (5) Bennett angle, (6) InitialBennett movement and (7) Curve of Spee, (ii) wherein the processor isfurther configured to reconstruct a 3D model of a face of the patientfrom the received images, (iii) wherein the processor is furtherconfigured to superimpose a scan of the intraoral cavity of the patienton the 3D model, (iv) wherein the processor is further configured todetermine the fixed and/or moving points by feature analysis, (v)wherein the feature analysis includes the use of deep learning todetermine fixed and/or moving points, (vi) wherein the fixed pointsinclude a location of a temporomandibular joint, (vii) wherein theprocessor is further configured to produce a restoration or a treatmentplan based on the articulator parameters.

In even yet another aspect of the present invention, a non-transitorycomputer-readable storage medium may be provided for storing a programwhich, when executed by a computer system, causes the computer system toperform a procedure comprising: receiving images of a patient's face;determining fixed and/or moving points in the patient's face based onthe received images, and calculating the articulator parameters based onthe determined fixed and/or moving points.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments will become more fully understood from the detaileddescription given herein below and the accompanying drawings, whereinlike elements are represented by like reference characters, which aregiven by way of illustration only and thus are not limitative of theexample embodiments herein and wherein:

FIG. 1 is a perspective view of a face of a patient illustrating itsrelationship with three planes of space.

FIG. 2 is a perspective view of a jaw illustrating a Bonwill triangleaccording to an exemplary embodiment of the present invention.

FIG. 3a is a perspective view of a jaw illustrating a Bennett angleaccording to an exemplary embodiment of the present invention.

FIG. 3b is a sketch illustrating movements of a patient's oral cavityand lips according to an exemplary embodiment of the present invention.

FIG. 4 is a side view of a jaw showing a curve of Spee according to anexemplary embodiment of the present invention.

FIG. 5 is a top view of a jaw showing an intercondylar distanceaccording to an exemplary embodiment of the present invention.

FIG. 6 is a side view of a jaw showing a sagittal condylar pathinclination according to an embodiment of the present invention.

FIG. 7 is a perspective view of a jaw illustrating a Balkwill angleaccording to an exemplary embodiment of the present invention.

FIG. 8 is a flow chart describing a method according to an exemplaryembodiment of the present invention.

FIG. 9 is a side view of a system illustrating fixed and moving pointsaccording to an exemplary embodiment of the present invention.

FIG. 10a is a side view of a jaw illustrating a first movement of apatient's oral cavity according to an exemplary embodiment of thepresent invention.

FIG. 10b is a side view of a jaw illustrating a second movement of apatient's oral cavity according to an exemplary embodiment of thepresent invention.

FIG. 11 is a block diagram showing a computer system according to anexemplary embodiment of the present invention.

Different ones of the figures may have at least some reference numeralsthat may be the same in order to identify the same components, althougha detailed description of each such component may not be provided belowwith respect to each Figure.

DETAILED DESCRIPTION OF THE INVENTION

In accordance with example aspects described herein, a method, systemand computer readable storage media may be provided for determiningarticulation parameters.

Method for Determining Articulation Parameters

Determining the articulation parameters of a specified patient such asintercondylar distance and Bennett angle is essential for the productionof a restoration. Various articulation parameters, described in moredetail hereinafter, provide important information about the natural andcorrect process of jaw movement during the chewing process. Theinformation obtained allows a correct articulation of the jaws as wellas an indication of the cusp and groove positions in teeth. This savesthe dentist and patient time and inconvenience due to later adaptions ofrestorations.

A goal of the invention is to relate parts of a patient's oral cavitysuch as the maxillary arch, mandibular arch and/or temporomandibularjoint to the face in planes of space through the use of images takenwith a depth sensor. This may be done for dentofacial analysis of themaxillary arch as well as for the establishment of functionalrelationships with the mandibular arch. The process of determining thearticulation parameters may be digitized through the use of images takenwith a depth sensor, thereby, removing the need for complex structuressuch as a physical facebow.

FIG. 1 illustrates imaginary planes passing through a 3D reconstructionof a patient's face 10 that preferably includes the jaws, the planesbeing sagittal plane S, a frontal plane F, and a horizontal plane H. Thesagittal plane S is an anatomical plane that may divide the patient'shead into two parts. Thus, the sagittal plane that divides the head intotwo halves is a mid-sagittal plane. The frontal plane F may be at rightangles with the sagittal plane S, and may divide the head into anteriorand posterior regions. The horizontal plane H may also be at rightangles to the sagittal plane and may divide the head into upper andlower regions.

A virtual articulator may enable a dentist to configure a restorationand/or examine the functioning of teeth, based on for example determinedlocations of virtual contact points between upper and lower jaws of thepatient's oral cavity. It may also enable an orthodontist to among otherthings, detect collisions between teeth as the teeth are moved/shifted,therefore allowing the detection of a difference between a normal bitesituation before any movements are carried out and the bite situationafter movement. In order to configure the virtual articulator, patientspecific inputs (patient specific articulation parameters) may beobtained from images of the patient's head, said images containinggeometric information that may be used to determine relative distancesbetween parts of the patient's dentition from each other as well as fromthe temporomandibular joint.

Said articulation parameters may include but may not be limited to (i)Sides 11 a of the Bonwill triangle 11 (arms), (ii) Intercondylardistance 15 (base of the Bonwill triangle), (iii) Balkwill angle 17,(iv) Sagittal condylar path inclination β, (v) Bennett angle α, (vi)Initial Bennett movement (Immediate side shift left/right), (vii) Curveof Spee 18. These parameters may be determined by tracking fixed points20 and/or moving points 21 on the patient's face using a camera system22 having a depth sensor 24 and determining geometrical distances andangles between these points and specified parts of the patient'sdentition as described hereinafter.

FIG. 2 illustrates the Bonwill triangle 11 which is an equilateraltriangle connecting the contact points of the mandibular centralincisors' 14 incisal edge (or the midline of the mandibular residualridge) to the midpoint of each condyle 13, and from one condyle 13 tothe other 13. Since it is an equilateral triangle, the length of thesides 11 a may be approximately equal (usually 4 inches). Knowing thelocation or approximate location of the right and left temporomandibularjoints 23 based on anatomical feature extraction methods discussedherein, the sides of the Bonwill triangle 11 may be computed.

FIG. 3a illustrates the Bennett angle α, which is the angle formedbetween the sagittal plane S and a path of the advancing condyle 13, asthe mandible moves laterally, when viewed on a horizontal plane H. Asshown in FIGS. 3a and 3b , as the mandible moves to the left (M₁), froma an initial position (M₀), an angle α is formed between the plane S andthe path of the advancing condyle 13 a, and point p on condyle 13 amoves from position P₁ to position P₂. Initial Bennett movement (alsoknown as Immediate side shift) refers to a movement in which bothcondyles may be displaced to the side at the start of lateral movementof the mandible; i.e., the whole mandible may perform a sidewaysmovement running parallel to the hinge axis before the nonworkingcondyle moves forward, downward, and inward. This side shift may beperformed with difficulty as an isolated movement, and it may beinterpreted as evidence of joint damage, e.g. capsule or ligamentstrain.

FIG. 4 shows the curve of Spee, which is a curvature of the mandibularocclusal plane beginning at the premolar and following the buccal cuspsof the posterior teeth, continuing to the terminal molar.

FIG. 5 illustrates the intercondylar distance 15 which is a distancebetween the two temporomandibular joints 23 of a patient (i.e. thedistance between the condyles 13). FIG. 6 is a diagram showing thesagittal condylar path inclination II, the angle formed by the condylarpath (path P-C, followed by the condyle 13 in the temporomandibularjoint 23) and the horizontal reference plan Hi. This may occur duringprotrusion (forward movement) of the mandible of the patient.

FIG. 7 shows the Balkwill angle 17 which is an angle between by theBonwill triangle 11 and the Balkwill triangle/occlusal triangle 16defined by the horizontal plane of the dentition of the mandible.Knowing these and other similar parameters for a virtual articulatorwithout the use of a physical facebow or physical articulator may helpreduce treatment times in a dentist's office.

Having described the exemplary articulator parameters, reference willnow be made to FIGS. 8-10, to illustrate a process S1000 that may beemployed in accordance with at least some of the example embodimentsherein. Process S1000 may begin at Step S100 wherein a plurality imagesof a patient performing predetermined masticatory/chewing movements maybe taken. The images may be taken using a camera system 22 having adepth sensor 24. For example, using a smartphone with depth sensors(such as the Lenovo Phab 2 Pro with Google Tango technology) or astand-alone combination of color and depth cameras, the patient's facemay be captured. The depth information may be used to reconstruct a 3Dmodel of the face 10, including the jaws, as shown in Step S200.Moreover, a series of 2D images may be captured while the patientperforms the predetermined masticatory movements and corresponding 3Dmodels of the face 10 may be reconstructed for each movement. Thereconstruction may be done in real time or may be done when necessaryand used for measurements. Alternatively, knowing how 2D images relateto each other with respect to position as well as their scale, distancesand angles, those images may be calculated without doing a 3Dreconstruction. In addition, movements may be recorded and/or tracked byidentifying relevant points and comparing them frame to frame. Theimages/recordings may be saved in a database. In an exemplaryembodiment, the images may be saved and tagged in the database forspecific articulation positions. The movements may include shoving thelower jaw (mandibular arch) in front of upper jaw (maxillary arch) andmoving the lower jaw left and right while upper jaw is in contact withthe lower jaw. The movements may be determined to correspond to thenatural movements of the patient which chewing as well as movements thatmay allow the collection of geometrical distances and angles needed forarticulation.

In Step S300, fixed points 20 and/or moving points 21 may be determinedby feature analysis. This may include the use of natural markers of thedental and facial anatomy preferably without the use of predeterminedartificial markers. However, in some embodiments, predeterminedartificial markers may be placed on predetermined positions on the faceand/or oral cavity and the fixed points 20 and/or moving points may bedetermined by recognition of the predetermined artificial markers. Thesefixed points 20 and/or moving points 21 may be determined to aid in thecalculation of various distances and angles. Fixed points 20 may help indetermining the orientation of the face during movement. The fixedpoints 20 and/or moving points 21 may preferably be points on the faceor oral cavity containing little or no soft tissue such as, for example,on the forehead 20 a, under the nose, 20 b, and those points on eitherside of the head 20 c where the temporomandibular joint 23 is anchoredand from which the movement originates. Some moving points 21 howevermay contain soft tissue (e.g. the lips).

Other natural markers may include bumps, fissures, position of the teethand their relation to each other artificial markers may includestickers, color dots with a pen, glued-on geometries and the like.

Moving points 21 may indicate points at which the displacements and/ormaximum distances corresponding to a particular masticatory movement maybe measured. For example, when the patient moves the jaw in a neededposition (depending on which articulation parameter is being measured),an image for a jaw in a first position (e.g. closed jaw) may be recordedand another image for the jaw in a second position (e.g. moved jaw) maybe obtained. For example, when the patient moves the lower jaw in amovement M₄ as shown in FIG. 9 moving point 21 moves from position 1 toposition 2. The distance between positions 1 and 2 can therefore beobtained from the images. Moreover, when the lower jaw moves away fromthe upper jaw in a movement M₃ as shown in FIGS. 10a and 10b , adisplacement d₁ between points P₃ and P₄ may then be identified andmeasured. Points P₃ and P₄, may be any points of interest and may not belimited to the molars alone. For example, they may define the contactpoints of the mandibular central incisors' incisal edge or the midlineof the mandibular residual ridge. Said points may also be located insimilar positions in humans and may therefore be determined by featureanalysis/extraction.

In embodiments where predetermined artificial markers are not used, thefixed points 20 and/or moving points 21 may be determined throughfeature analysis of anatomical structures. Feature analysis may includedefining features of the dentition that may be recognized and used indetecting larger structures. The features may include smaller points,edges, objects on an image and/or curves or boundaries definingdifferent regions of an image. In an exemplary embodiment herein,feature analysis may include machine/deep learning methods wherein acomputer may be trained to recognize fixed points 20 and moving points21 based on previously classified images of faces. For example, startingwith a plurality of images of human faces, a dental practitioner maylabel pixels of the images to be used for training by marking areas ofinterest e.g. the fixed points 20 and/or moving points 21. The markingof the training images may be done digitally e.g. by setting dots on theimages corresponding to the points of interest. Using this set oflabeled or classified images, a network architecture/deep neural networksuch as a convolutional neural network (CNN) may be built and fed withthe labeled pictures allowing the network to “learn” from it such thatthe network may produce a network wiring that may classify new images onits own. After the training, the network may be given previously unseenimages and the output, such as a location probability vector containinglocation probability values wherein the highest location probabilityvalues may define locations of the fixed 20 and/or moving points 21 maybe obtained and corresponding feedback may be given such that thenetwork may preferably operate on its own eventually to classify imageswithout human help. The images used for training may also be derivedfrom or otherwise based on ongoing patient treatments, past patienttreatments, the results of patient treatments or otherwise treatmentinformation.

After obtaining the fixed points 20 and/or moving points 21, the valuesof the articulator parameters may be obtained using geometricaldistances and angles measured with the obtained fixed points 20 and/ormoving points 21 as shown in Step S400. Some articulation parameters maybe measured using just the fixed points 20 or single images while otherarticulation parameters may be obtained using the fixed points 20 andthe moving points 21 or multiple images. For example, in determining themaximum displacement of displacement d₁ during movement M₃, there may bemultiple images depicting multiple locations of the lower jaw. Imageswith the biggest difference regarding points of interest may be obtainedand the Euclidean distance between those points may be calculated. Usingan extracted or recognized point representing the location of thetemporomandibular joint 23 along with the displacement d₁ and/or otherfixed points 20 and moving points 21, Euclidean geometry employing theuse of lines, angles and triangles or coordinates in space obtainedfrom, for example, 3D reconstruction or images may be used to measurevalues of the articulation parameters. A computer system 100 may thus beprogrammed to carry out these steps repeatedly by means of an algorithmthat combines the deep learning feature extraction or simpler objectrecognition methods such as gradient matching or template matching orthe like with methods involving the measuring of relevant distances andangles.

Other systems and methods for dental measurements may include thosetaught in U.S. Pat. No. 8,126,726B2, entitled “System and method forfacilitating automated dental measurements and diagnostics”, by Matov etal, which is incorporated by reference herein in its entirety, as if setforth fully herein.

After obtaining the values for the articulation parameters, a virtualarticulator may be filled with said values as shown in Step S500. Thefilled in virtual articulator may then be used to complete a treatmentprocedure, Step S600, such as in creating a restoration or analyzing thecorrect function of teeth 12 of the patient. Moreover, the obtainedvalues may be transmitted, for example wirelessly, to a CAD/CAMsoftware, wherein the values may be integrated and used for thecalculation of a functionally proper proposal of a restoration or otherdental or orthodontic appliance.

Further, the 3D reconstruction of the face 10 may be superimposed withan intraoral data record, such as a 3D measurement/scan of the intraoralcavity of the patient taken with an intraoral scanner, for example, inorder to visualize the restoration or treatment planning and/or resultsof the treatment planning. The superimposition may be done byidentifying common regions in both data such as teeth and overlaying thedata together along the common regions.

Preferably the process S1000 may be completed in a short time period(e.g. a few seconds to 2 minutes) as compared to about 45 minutes in theuse of a physical facebow and/or physical virtual articulator. Moreover,potentially higher precision may be achieved, and the process may alsopreferably drastically reduce the cost of articulation and user errors,leading to a higher application rates. In addition, the process may alsobe comfortable for patients resulting in a higher customer satisfactionwith the dentist.

System for Determining Articulation Parameters

Having described the system 1 of FIG. 9 reference will now be made toFIG. 11, which shows a block diagram of a computer system 100 that maybe employed in accordance with at least some of the example embodimentsherein. Although various embodiments may be described herein in terms ofthis exemplary computer system 100, after reading this description, itmay become apparent to a person skilled in the relevant art(s) how toimplement the disclosure using other computer systems and/orarchitectures.

In one example embodiment, the computer system may include a camerasystem 22 which may operate under one of several depth sensingprinciples including, for example, (i) structural light, (ii) Time ofFlight (ToF) and/or (iii) stereoscopic principles. For cameras employingstructural light, a light source may be used to project a known patternonto the patient or patient's head, and a receiver may detect thedistortion of the reflected pattern to calculate depth map based ongeometry. For cameras employing Time of Flight (ToF) principles, a lightsource may send out a pulse, and a sensor may detect a reflection of thepulse from the patient in order to record it's time of flight. Knowingthat and the constant speed of light, the system may calculate how faraway the points on the patient's head. Alternatively, a modulated lightsource may be send and a phase change of light reflected from thepatient may be detected. For cameras employing stereoscopic principles,multiple cameras may be placed at different positions to capturemultiple images of the patient's head, and a depth map may be calculatedbased on geometry. This depth information may be used to track thepatient's head during treatment.

The computer system 100 may also include at least one computer processor122, user interface 126 and input unit 130. The input unit 130 in oneexemplary embodiment may be used by the dentist along with a displayunit 128 such as a monitor to send instructions or requests about thecapturing of images of the face, the reconstruction of 3D images of theface 10, the determination of fixed points 20 and moving points 21and/or the calculation of articulator values. They may also be used fortreatment planning including for example, the creation of a restoration.In another exemplary embodiment herein, the input unit 130 is a fingeror stylus to be used on a touchscreen interface (not shown). The inputunit 130 may alternatively be a gesture/voice recognition device, atrackball, a mouse or other input device such as a keyboard or stylus.In one example, the display unit 128, the input unit 130, and thecomputer processor 122 may collectively form the user interface 126.

The computer processor 122 may include, for example, a centralprocessing unit, a multiple processing unit, an application-specificintegrated circuit (“ASIC”), a field programmable gate array (“FPGA”),or the like. The processor 122 may be connected to a communicationinfrastructure 124 (e.g., a communications bus, or a network). In anembodiment herein, the processor 122 may receive a request forautomatically capturing images of the face, automatically storing theimages in a database, automatically reconstructing a 3D model of theface 10, automatically determining fixed points 20 and moving points 21,automatically calculating and filling in articulator parameters and/orautomatically producing a treatment plan, restoration or analysis of thefunctioning of teeth 12. The processor 122 may then load saidinstructions and execute the loaded instructions such as using adatabase or artificial intelligence (AI) to obtain treatments fordisplay.

One or more steps/procedures for determining articulator parameters maybe stored on a non-transitory storage device in the form ofcomputer-readable program instructions. To execute a procedure, theprocessor 122 loads the appropriate instructions, as stored on a storagedevice, into memory and then executes the loaded instructions.

The computer system 100 may further comprise a main memory 132, whichmay be a random access memory (“RAM”) and also may include a secondarymemory 134. The secondary memory 134 may include, for example, a harddisk drive 136 and/or a removable-storage drive 138 (e.g., a floppy diskdrive, a magnetic tape drive, an optical disk drive, a flash memorydrive, and the like). The removable-storage drive 138 may read fromand/or write to a removable storage unit 140 in a well-known manner. Theremovable storage unit 140 may be, for example, a floppy disk, amagnetic tape, an optical disk, a flash memory device, and the like,which may be written to and read from by the removable-storage drive138. The removable storage unit 140 may include a non-transitorycomputer-readable storage medium storing computer-executable softwareinstructions and/or data.

In further alternative embodiments, the secondary memory 134 may includeother computer-readable media storing computer-executable programs orother instructions to be loaded into the computer system 100. Suchdevices may include a removable storage unit 144 and an interface 142(e.g., a program cartridge and a cartridge interface); a removablememory chip (e.g., an erasable programmable read-only memory (“EPROM”)or a programmable read-only memory (“PROM”)) and an associated memorysocket; and other removable storage units 144 and interfaces 142 thatallow software and data to be transferred from the removable storageunit 144 to other parts of the computer system 100.

The computer system 100 also may include a communications interface 146that enables software and data to be transferred between the computersystem 100 and external devices. Such an interface may include a modem,a network interface (e.g., an Ethernet card or a wireless interface), acommunications port (e.g., a Universal Serial Bus (“USB”) port or aFireWire® port), a Personal Computer Memory Card InternationalAssociation (“PCMCIA”) interface, Bluetooth®, and the like. Software anddata transferred via the communications interface 146 may be in the formof signals, which may be electronic, electromagnetic, optical or anothertype of signal that may be capable of being transmitted and/or receivedby the communications interface 146. Signals may be provided to thecommunications interface 146 via a communications path 148 (e.g., achannel). The communications path 148 may carry signals and may beimplemented using wire or cable, fiber optics, a telephone line, acellular link, a radio-frequency (“RF”) link, or the like. Thecommunications interface 146 may be used to transfer software or data orother information between the computer system 100 and a remote server orcloud-based storage (not shown).

One or more computer programs or computer control logic may be stored inthe main memory 132 and/or the secondary memory 134. The computerprograms may also be received via the communications interface 146. Thecomputer programs may include computer-executable instructions which,when executed by the computer processor 122, cause the computer system100 to perform the methods as described hereinafter.

In another embodiment, the software may be stored in a non-transitorycomputer-readable storage medium and loaded into the main memory 132and/or the secondary memory 134 of the computer system 100 using theremovable-storage drive 138, the hard disk drive 136, and/or thecommunications interface 146. Control logic (software), when executed bythe processor 122, causes the computer system 100, and more generallythe system for determining articulator parameters, to perform all orsome of the methods described herein.

In another example embodiment, the computer system 100 may be a mobiledevice such as a smartphone having an application that may be engaged bya user to propose and visualize dental and orthodontic treatments.

Implementation of other hardware arrangement so as to perform thefunctions described herein will be apparent to persons skilled in therelevant art(s) in view of this description.

What is claimed is:
 1. A method for determining articulation parameters,the method comprising the steps of: receiving images of a patient'sface; determining defined fixed and/or moving points in the images ofthe patient's face, for calculating articulation parameters, using atrained neural network, the trained neural network being trained basedon a dataset of labelled training images to identify points of interestcorresponding to one or more fixed and/or moving training points bymapping the one or more fixed and/or moving training points in thetraining images to one or more highest location probability values of alocation probability vector, the one or more highest locationprobability values being indicative of a location of the fixed and/ormoving training points; computing a set of geometrical distances andangles between the determined fixed and/or moving points; calculating,responsive to the determining step, the articulator parameters based onthe geometrical distances and/or angles measured using the determinedfixed and/or moving points; and, using the calculated articulatorparameters to fill a virtual articulator with articulation values. 2.The method according to claim 1, wherein the articulator parameters arechosen from the group consisting of (i) Sides of a Bonwill triangle,(ii) Intercondylar distance, (iii) Balkwill angle, (iv) Sagittalcondylar path inclination, (v) Bennett angle, (vi) Initial Bennettmovement and (vii) Curve of Spee.
 3. The method according to claim 1,further comprising reconstructing a 3D model of a face of the patientfrom the received images.
 4. The method according to claim 1, furthercomprising superimposing a scan of the intraoral cavity of the patienton the 3D model.
 5. The method according to claim 1, wherein the fixedpoints include a location of a temporomandibular joint.
 6. The methodaccording to claim 1, further comprising analyzing the functioning ofteeth based on the articulator parameters.
 7. The method according toclaim 1, further comprising producing a restoration or a treatment planbased on the articulator parameters.
 8. The method according to claim 1,wherein the trained neural network is a convolutional neural network. 9.A system for determining articulation parameters, the system comprisingat least one processor configured to perform the steps of: receivingimages of a patient's face; determining defined fixed and/or movingpoints in the images of the patient's face, for calculating articulationparameters, using a trained neural network, the trained neural networkbeing trained based on a dataset of labelled training images to identifypoints of interest corresponding to one or more fixed and/or movingtraining points by mapping the one or more fixed and/or moving trainingpoints in the training images to one or more highest locationprobability values of a location probability vector, the one or morehighest location probability values being indicative of a location ofthe fixed and/or moving training points; computing a set of geometricaldistances and angles between the determined fixed and/or moving points;calculating, responsive to the determining step, the articulatorparameters based on the geometrical distances and/or angles measuredusing the determined fixed and/or moving points; and, using thecalculated articulator parameters to fill a virtual articulator witharticulation values.
 10. The system according to claim 9, wherein theprocessor is further configured to choose the articulator parametersfrom the group consisting of (i) Sides of a Bonwill triangle, (ii)Intercondylar distance, (iii) Balkwill angle, (iv) Sagittal condylarpath inclination, (v) Bennett angle, (vi) Initial Bennett movement and(vii) Curve of Spee.
 11. The system according to claim 9, wherein theprocessor is further configured to reconstruct a 3D model of a face ofthe patient from the received images.
 12. The system according to claim9, wherein the processor is further configured to superimpose a scan ofthe intraoral cavity of the patient on the 3D model.
 13. The systemaccording to claim 9, wherein the fixed points include a location of atemporomandibular joint.
 14. The system according to claim 9, whereinthe processor is further configured to produce a restoration or atreatment plan based on the articulator parameters.
 15. A non-transitorycomputer-readable storage medium storing a program which, when executedby a computer system, causes the computer system to perform a procedurecomprising: receiving images of a patient's face; determining definedfixed and/or moving points in the images of the patient's face, forcalculating articulation parameters, using a trained neural network, thetrained neural network being trained based on a dataset of labelledtraining images to identify points of interest corresponding to one ormore fixed and/or moving training points by mapping the one or morefixed and/or moving training points in the training images to one ormore highest location probability values of a location probabilityvector, the one or more highest location probability values beingindicative of a location of the fixed and/or moving training points;computing a set of geometrical distances and angles between thedetermined fixed and/or moving points; calculating, responsive to thedetermining step, the articulator parameters based on the geometricaldistances and/or angles measured using the determined fixed and/ormoving points; and, using the calculated articulator parameters to filla virtual articulator with articulation value.